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## Introduction:
Wellspring is a Canada-wide network of charities offering free cancer support programs to individuals at any stage of their cancer journey. On March 4, 2024, Wellspring implemented a simplified registration system with the goal of increasing member engagement. This project aims to analyze how this change, along with other factors, influences member engagement in Wellspring’s programs. Specifically, we will explore three key questions:
1. How do age and program interests influence average monthly attendance?
Using multiple linear regression, we’ll analyze these relationships to help Wellspring tailor programs for different age groups and interests.
2. Does POC (Person of Color) status impact a member’s total amount of service attended?
A hypothesis test will assess whether attendance differs by POC status, helping Wellspring ensure equitable access to programs.
3. Can registration date, mailing province, and cancer risk predict attendance in the past three months?
Classification trees will be used to predict attendance, enabling Wellspring to identify members needing additional support and allocate resources effectively.
 
## Population and Audience:
Population: All potential and current members of Wellspring in Canada, including cancer patients and their families.
Audience: Wellspring staff, who are experts in cancer support services but may not have formal training in statistics. Our findings will be presented with clear, actionable insights to help them improve program delivery and member engagement.
 
## Question 1: Age, Program Interests, and Average Monthly Attendance
 
Variables Used:
- age_years: Age of the member.
- num_program_interests: Number of programs the member is interested in.
- number_of_present_service_deliveries: Number of services the member attended.
- member_start_year and member_start_month: Year and month the member joined Wellspring.
Data Wrangling:
Filtered members with valid age data (age_years >= 0).
Created a new variable, degree_of_focus, categorizing members based on their number of program interests:
- 1 program of interest
- 2-4 programs of interest
- 5-7 programs of interest
- No program of interest
Calculated average_attendance_per_month as:
Average Attendance = Number of Services Attended / (2024 - Member Start Year) * 12 + (12 - Member Start Month + 1)
note that a member joined in 2024-12 is considered as 1 month, and 2024-11 is considered as 2 months, 2023-10 is considered 15 months. This is to avoid division by 0 whilst preserving many of the events that were held during the last month (2024-12) as we have observed. ?
Visualization:
A scatter plot with a linear regression line showing the relationship between age, program interests, and average monthly attendance (Figure 1).
A table summarizing the count of members by their degree of focus.

## Plot

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![](TUT0202-B-Final-Project_files/figure-revealjs/unnamed-chunk-1-1.png){width=960}
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A tibble: 4 × 2

degree_of_focus n 1 1 program of interest 101 2 2-4 programs of interest 162 3 5-7 programs of interest 94 4 no program of interest 2492

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## Conclusion: 1. Age, Program Interests, and Average Monthly Attendance
We found that both age and the number of program interests influence average monthly attendance. Older members and those with more program interests tend to have higher attendance rates. This suggests that Wellspring could:
Develop targeted programs for younger members to increase their engagement.
Encourage members to explore multiple programs by promoting cross-program participation.
 
## Question 2: Impact of POC Status on Engagement

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Variables Used:

- member_id: Unique ID of the member
- i_identify_as_poc: Whether the member identifies as a Person of Color (POC).
- attendance_status: Attendance records for each member.

Hypothesis Test:

- Conducted a randomization test to compare mean attendance between POC and non-POC members.
- Calculate the p-value to assess the significance of the observed difference.
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Data Wrangling:

- Grouped attendance records by member_id to calculate total_attendance (number of services attended).
- Joined attendance data with POC status data.
- Filtered out members with missing POC status.
- Created scenarios to categorize members:
    1. Never signed up in 2023-2024.
    2. Signed up but never attended.
    3. Attended zero events.

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## Diagram 2

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![](TUT0202-B-Final-Project_files/figure-revealjs/unnamed-chunk-2-1.png){width=960}
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## Diagram 3 and Table of inactive member

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![](TUT0202-B-Final-Project_files/figure-revealjs/unnamed-chunk-3-1.png){width=960}
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A tibble: 3 × 2

scenario people 1 people never sign up in 2023-2024 105 2 people sign up only once but not showing up 5 3 all people that attend 0 event 110

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## Test statistic and P-value

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